mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-29 19:15:04 +08:00
148 lines
5.3 KiB
Python
148 lines
5.3 KiB
Python
import logging
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import os
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# os.system("wget -P cvec/ https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt")
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import gradio as gr
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from dotenv import load_dotenv
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from configs.config import Config
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from i18n.i18n import I18nAuto
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from infer.modules.vc.modules import VC
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logging.getLogger("numba").setLevel(logging.WARNING)
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logging.getLogger("markdown_it").setLevel(logging.WARNING)
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logging.getLogger("urllib3").setLevel(logging.WARNING)
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logging.getLogger("matplotlib").setLevel(logging.WARNING)
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logger = logging.getLogger(__name__)
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i18n = I18nAuto()
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logger.info(i18n)
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load_dotenv()
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config = Config()
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vc = VC(config)
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weight_root = os.getenv("weight_root")
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weight_uvr5_root = os.getenv("weight_uvr5_root")
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index_root = os.getenv("index_root")
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names = []
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hubert_model = None
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for name in os.listdir(weight_root):
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if name.endswith(".pth"):
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names.append(name)
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index_paths = []
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for root, dirs, files in os.walk(index_root, topdown=False):
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for name in files:
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if name.endswith(".index") and "trained" not in name:
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index_paths.append("%s/%s" % (root, name))
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app = gr.Blocks()
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with app:
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with gr.Tabs():
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with gr.TabItem("在线demo"):
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gr.Markdown(
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value="""
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RVC 在线demo
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"""
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)
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sid = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
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with gr.Column():
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spk_item = gr.Slider(
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minimum=0,
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maximum=2333,
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step=1,
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label=i18n("请选择说话人id"),
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value=0,
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visible=False,
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interactive=True,
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)
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sid.change(fn=vc.get_vc, inputs=[sid], outputs=[spk_item])
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gr.Markdown(
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value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ")
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)
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vc_input3 = gr.Audio(label="上传音频（长度小于90秒）")
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vc_transform0 = gr.Number(label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0)
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f0method0 = gr.Radio(
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label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
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choices=["pm", "harvest", "crepe", "rmvpe"],
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value="pm",
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interactive=True,
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)
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filter_radius0 = gr.Slider(
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minimum=0,
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maximum=7,
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label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
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value=3,
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step=1,
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interactive=True,
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)
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with gr.Column():
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file_index1 = gr.Textbox(
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label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
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value="",
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interactive=False,
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visible=False,
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)
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file_index2 = gr.Dropdown(
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label=i18n("自动检测index路径,下拉式选择(dropdown)"),
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choices=sorted(index_paths),
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interactive=True,
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)
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index_rate1 = gr.Slider(
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minimum=0,
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maximum=1,
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label=i18n("检索特征占比"),
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value=0.88,
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interactive=True,
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)
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resample_sr0 = gr.Slider(
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minimum=0,
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maximum=48000,
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label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
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value=0,
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step=1,
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interactive=True,
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)
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rms_mix_rate0 = gr.Slider(
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minimum=0,
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maximum=1,
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label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
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value=1,
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interactive=True,
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)
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protect0 = gr.Slider(
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minimum=0,
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maximum=0.5,
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label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
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value=0.33,
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step=0.01,
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interactive=True,
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)
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f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
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but0 = gr.Button(i18n("转换"), variant="primary")
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vc_output1 = gr.Textbox(label=i18n("输出信息"))
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vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))
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but0.click(
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vc.vc_single,
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[
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spk_item,
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vc_input3,
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vc_transform0,
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f0_file,
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f0method0,
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file_index1,
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file_index2,
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# file_big_npy1,
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index_rate1,
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filter_radius0,
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resample_sr0,
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rms_mix_rate0,
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protect0,
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],
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[vc_output1, vc_output2],
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)
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app.launch()
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